pr-review-coachlisted

Companion to the `pr-atom-reviewer` skill. Reads the review history stored in AllSource Prime (the `prime_*` MCP tools) and turns it into actionable improvements to how the reviewer works. Three jobs — (1) ingest post-merge outcomes for past reviews (was the split followed? did the PR cause a revert?), (2) cluster the repo's actual atom vocabulary so the calibration table reflects this team's work instead of generic examples, (3) surface contradictions across past reviews so the user can decide what to harden into the SKILL.md. Use this skill when the user asks "what have we learned from reviews", "audit my past reviews", "what should I tighten in the reviewer skill", "did my recommended splits get followed", "how is the reviewer doing", "weekly review retro", or runs a periodic skill-improvement pass. Trigger also on phrases like "PR retro", "review postmortem", "calibrate my reviewer", or when the user mentions wanting to update the calibration table or the SKILL.md for the reviewer.
decebal/curated-claude-skills · ★ 0 · Code & Development · score 58

Install: claude install-skill decebal/curated-claude-skills

# PR Review Coach A meta-skill that improves the `pr-atom-reviewer` skill over time by reading from AllSource Prime and producing concrete edits the user can apply. This skill runs in Claude Code in the terminal. It expects: - The `prime_*` MCP tools (from `allsource-prime`) to be available. - The `pr-atom-reviewer` skill to be installed and to have recorded at least a few reviews via its Step 7. - `git`, `gh` (GitHub CLI), and `jq` available for outcome-tracking jobs. If Prime isn't available, stop and tell the user: this skill is useless without the memory store the reviewer writes to. Direct them to install `allsource-prime` and configure it as an MCP server in Claude Code: ```bash cargo install allsource-prime claude mcp add prime -s user -- allsource-prime --data-dir ~/.prime/memory ``` Then re-run the reviewer on a few PRs to build up a history before invoking this skill. ## The three jobs (Loops 2–4) This skill has three distinct jobs. Always ask the user which one they want first; don't try to run all three in one go — each produces a focused output worth reviewing on its own. ``` 1. ingest-outcomes — Loop 2: tag past reviews with what happened after 2. cluster-vocab — Loop 3: build a repo-specific calibration table 3. find-drift — Loop 4: surface contradictions across past reviews ``` If the user says "do all of them", run them in that order — outcomes first (it's the most valuable signal), vocab second (it depends on having outcome-tagged reviews),